How machine learning models can amplify inequities in medical diagnosis and treatment
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
MIT researchers investigate the causes of health-care disparities among underrepresented groups.
Predictions from the OncoNPC model could enable doctors to choose targeted treatments for difficult-to-treat tumors.
MIT researchers develop “FrameDiff,” a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
MIT alumnus’ platform taps the wisdom of crowds to label medical data for AI companies.
With the artificial intelligence conversation now mainstream, the 2023 MIT-MGB AI Cures conference saw attendance double from previous years.
Leo Anthony Celi invites industry to broaden its focus in gathering and analyzing clinical data for every population.
A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
Aided by machine learning, scientists are working to develop a vaccine that would be effective against all SARS-CoV-2 strains.
The program leverages MIT’s research expertise and Takeda’s industrial know-how for research in artificial intelligence and medicine.